Method for rescheduling flights affected by a disruption and an airline operations control system and controller

ABSTRACT

An airline operations system, controller and method reschedules flights affected by a disruption that precludes a planned schedule for the flights. The method includes obtaining data related to a scheduled origination and a scheduled destination for each of a set of passengers scheduled on the flights, generating a passenger connection network of connections between the scheduled origination and the scheduled destination for a subset of the set of passengers, applying at least one criterion to the passenger connection network and rescheduling at least one connecting flight based on the criteria. The system and controller solves a network flow problem to reschedule a subset of connecting flights to have a delayed projected departure time. Resulting output can include the set of delayed flights along with modified projected departure times, a set of flight cancellations and a set of passengers with missed connections.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/713,325, filed on Sep. 15, 2015, titled “METHOD FOR RESCHEDULINGFLIGHTS AFFECTED BY A DISRUPTION AND AN AIRLINE OPERATIONS CONTROLSYSTEM AND CONTROLLER”, which is herein incorporated by reference.

BACKGROUND OF THE INVENTION

Common sources of disruption to airline schedules include crew absences,mechanical failure, inclement weather, etc. Disruption events can occurwithout any notification and with immediate effect such as when anairport closes due to a radar failure. Other disruption events, such asmight occur because of deteriorating weather conditions, can have a moregradual effect on airline schedules. Planned airport or airspaceclosures cause disruption events with a defined time period where thedefined time period can be static or can dynamically change during thedisruption event.

In response to a disruption event, airlines reschedule their operationsby implementing recovery plans for schedule, aircraft, crews andpassengers that can include delaying or canceling flights, normally viaan airlines operations control center (AOCC). Operators in an AOCC of amajor airline manage the execution of hundreds or thousands of flights aday and adjust in real time the movements of the aircraft andcrewmembers of the airline to minimize costly delays and cancellations,while complying with complex maintenance and routing constraints. Theseoperators are responsible for preparing flight plans, adjusting theairline schedule including flight schedule, departure slot assignments,aircraft assignments and crew assignments in response to variousdisruption events. A challenge for major airlines is to limitinefficiency in the airline and manage information efficiently toalleviate the impact of unforeseen schedule disruptions.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, an embodiment relates to a method of reschedulingflights. The method includes obtaining itinerary data related to flightsfrom a scheduled origination to a scheduled destination for a set ofpassengers; generating, based on the data, a passenger connectionnetwork for a subset of the set of passengers that includes at least oneintermediate connecting flight having a first projected departure time;creating in the passenger connection network at least one alternateintermediate connecting flight having a second projected departure timethat is after the first projected departure time; adding a set ofconnections to the passenger connection network between the scheduledorigination and the scheduled destination for the subset of the set ofpassengers wherein the added set of connections includes the at leastone alternate intermediate connecting flight; applying at least onecriterion to the passenger connection network with the addedconnections; and rescheduling the subset of the set of passengers to theat least one alternate intermediate connecting flight at the secondprojected departure time based on the at least one criterion.

In another aspect, an embodiment relates to an airline operationscontrol system. The system includes a computing device including amemory configured to store instructions; and a processor configured toexecute the instructions. The instructions perform a method comprisingobtaining itinerary data related to flights from a scheduled originationto a scheduled destination for each of a set of passengers; generating,based on the data, a passenger connection network for a subset of theset of passengers that includes at least one intermediate connectingflight having a first projected departure time; creating in thepassenger connection network at least one alternate intermediateconnecting flight having a second projected departure time after thefirst projected departure time; adding a set of connections to thepassenger connection network between the scheduled origination and thescheduled destination for the subset of the set of passengers whereinthe added set of connections includes the at least one alternateintermediate connecting flight; applying at least one criterion to thepassenger connection network with the added connections; andrescheduling the subset of the set of passengers to the at least onealternate intermediate connecting flight at the second projecteddeparture time based on the at least one criterion.

In another aspect, an embodiment relates to a controller incommunication with a database containing data related to a scheduledorigination and a scheduled destination for each of a set of passengersscheduled on aircraft flights. The controller has software configured toaccess and retrieve the data; generate a passenger connection network ofconnections between the scheduled origination and the scheduleddestination for a subset of the set of passengers wherein theconnections include at least one connecting flight that is not directbetween the scheduled origination and the scheduled destination, whereinthe at least one connecting flight has a first projected departure time;create at least one copy of the at least one connecting flight having asecond projected departure time later than the first projected departuretime; add connections to the passenger connection network between thescheduled origination and the scheduled destination for the subsetwherein the added connections include the at least one copy of the atleast one connecting flight; apply at least one criterion to thepassenger connection network with the added connections; and reschedulethe at least one connecting flight to at least one second projecteddeparture time based on the at least one criterion.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an airline operations center that can practiceembodiments of the invention and is in communication with severalairports to reschedule flights during a disruption event.

FIG. 2 is a diagram graphically depicting a passenger connection from ascheduled inbound flight to an outbound flight where the inbound flightcan be delayed or canceled.

FIG. 3 is a block diagram illustrating an airline operations controlsystem and controller according to an embodiment of the invention.

FIG. 4 illustrates a flight network with five flight legs and fourpassenger connections.

FIG. 5 illustrates a passenger connection network according to anembodiment of the invention.

FIG. 6 illustrates an example of a flight network where a disruptionevent can be mitigated by embodiments of the invention.

FIG. 7 illustrates the flight network of FIG. 6 where flight delays areconsidered to mitigate a disruption event by embodiments of theinvention.

FIG. 8 illustrates a passenger connection network applied to the flightnetwork of FIG. 6 according to an embodiment of the invention.

FIG. 9 is a flow chart illustrating a method of rescheduling flightsaffected by a disruption according to an embodiment of the invention.

DESCRIPTION OF EMBODIMENTS OF THE INVENTION

It will be understood that details of environments that can implementembodiments of the invention are set forth in order to provide athorough understanding of the technology described herein. It will beevident to one skilled in the art, however, that the exemplaryembodiments can be practiced without these specific details. Thedrawings illustrate certain details of specific embodiments thatimplement a module or method, or computer program product describedherein. However, the drawings should not be construed as imposing anylimitations that can be present in the drawings. The method and computerprogram product can be provided on any machine-readable media foraccomplishing their operations. The embodiments can be implemented usingan existing computer processor, or by a special purpose computerprocessor incorporated for this or another purpose, or by a hardwiredsystem.

As noted above, embodiments described herein can include a computerprogram product comprising machine-readable media for carrying or havingmachine-executable instructions or data structures stored thereon. Suchmachine-readable media can be any available media, which can be accessedby a general purpose or special purpose computer or other machine with aprocessor. By way of example, such machine-readable media can compriseRAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other medium thatcan be used to carry or store desired program code in the form ofmachine-executable instructions or data structures and that can beaccessed by a general purpose or special purpose computer or othermachine with a processor. When information is transferred or providedover a network or another communication connection (either hardwired,wireless, or a combination of hardwired or wireless) to a machine, themachine properly views the connection as a machine-readable medium.Thus, any such a connection is properly termed a machine-readablemedium. Combinations of the above are also included within the scope ofmachine-readable media. Machine-executable instructions comprise, forexample, instructions and data, which cause a general-purpose computer,special purpose computer, or special purpose processing machines toperform a certain function or group of functions.

Embodiments will be described in the general context of method stepsthat can be implemented in one embodiment by a program product includingmachine-executable instructions, such as program codes, for example, inthe form of program modules executed by machines in networkedenvironments. Generally, program modules include routines, programs,objects, components, data structures, etc. that have the technicaleffect of performing particular tasks or implement particular abstractdata types. Machine-executable instructions, associated data structures,and program modules represent examples of program codes for executingsteps of the method disclosed herein. The particular sequence of suchexecutable instructions or associated data structures represent examplesof corresponding acts for implementing the functions described in suchsteps.

Understanding the embodiments disclosed herein will be aided by aninitial explanation of an airline environment and the problems facedwhen airline operations personnel make decisions with respect todisruption events. As airline networks can have hundreds of aircraft andthousands of crewmembers and extensive maintenance operations withintheir purview and take into consideration a wide variety of informationit will be understood that FIG. 1 only schematically illustrates a verysimplified version of an airline and the information which can be takeninto consideration. However, for simplicity of explanation, theexemplary situation of FIG. 1 is useful to explain the inventiveconcepts without undue complexity. More specifically, an airline havingthree aircraft 13, 15, and 17, which are respectively located at a firstairport 12, a second airport 14 and a third airport 16 and an airlinesoperations control center (AOCC) 10 are illustrated. While the aircraft13, 15, and 17 are illustrated as being identical, it will beappreciated that the aircraft 13, 15, and 17 can be different makes andmodels, with differing functionality and capacity, and thus can notnecessarily be optimal to be swapped with one another. The AOCC 10 is incommunication with the airports 12, 14, 16 by a computer network 18which can, for example, be a local area network or a larger network suchas the internet.

As aircraft have planned routes, consider a scenario where aircraft 13is scheduled to fly from airport 12 to airport 14 and then to airport16. Aircraft 15 is scheduled to fly from airport 14 to airport 16 andback to airport 14, and aircraft 17 is scheduled to fly from airport 16to airport 12. Passengers aboard the aircraft typically have diverseitineraries with various connecting flights to convey them from theirsources to their destinations. Consequently, while some of thepassengers departing from airport 12 can have a scheduled destination ofairport 14 or 16, others will arrive at these airports as anintermediate destination to connect to one or more additional flights.Therefore, if prior to the departure of the aircraft 13, the firstairport 12 experiences a disruption event, aircraft operations personnelof the AOCC 10 can either delay the flight until the disruption event isresolved at the airport 12 or cancel the flight, which can result inlarge costs to the airline and general customer dissatisfaction.

To reschedule operations, the AOCC 10 can employ a procedure foroperations recovery where operations recovery is a method to determineflight delays and cancellations in response to a disruption event.Embodiments of the system and method for operations recovery presentedherein include a model that considers delay options for outbound flightlegs to hold for connecting passengers. An operations recovery solverfor processing the model can be implemented on a computing device 11located at the AOCC. The computing device 11 is capable of executingsoftware to assist personnel at the AOCC in responding to the disruptionevent. For brevity, the computing device 11 executing softwareconfigured to perform operations recovery procedures is referred toherein as a “processor.”

FIG. 2 is a diagram graphically depicting a passenger connection from ascheduled inbound flight 20 to an outbound flight 24 where the inboundflight can be delayed or canceled. When a late inbound flight 22 arrivesa duration of time 26 after the listed arrival time of the scheduledinbound flight 20, passengers can experience a misconnection to theiroutbound flight 24 if the late inbound flight 22 arrives after thedeparture time of the outbound flight (as shown by the duration 28).Conventional operations recovery addresses missed passenger connectionsby penalizing a late inbound flight 22 that departs after the scheduleddeparture time for the outbound flight 24. However, conventionaloperations recovery enforces the penalty even if the outbound flight 24is also delayed such that a passenger can make the connection betweenthe late inbound flight 22 and the delayed outbound flight. That is,conventional operations recovery does not include a model where theoutbound flight 24 can be delayed to reduce the penalty associated withmisconnection. In other words, conventional operational recoveryincludes an objective function that is only an approximation thatconsiders an outbound flight's scheduled departure time withoutconsidering the relationship between the flight times of a passenger'sconnecting flight.

Referring now to FIG. 3, a block diagram is shown that illustrates anairlines operations control system 100 and, particularly, a controller110 configured to reschedule flights affected by a disruption accordingto embodiments of the invention. The airline operations control system100 includes a computing device shown in FIG. 3, but not limited to, acontroller 110 (and herein referred to as “controller”). The controller110 can include, but is not limited to a memory configured to storeinstructions for execution by a processor. The instructions and theirstorage can include any suitable computing modalities, architecture andframework including, but not limited to hardware, software andcombinations thereof. The controller 110, by way of a flight disruptioncomponent 113 is configured to obtain data related to the scheduledorigination and destination for each of a set of passengers scheduled onthe flights. The data is stored on any suitable computing storage mediumincluding, but not limited to a database of passenger schedule data 116.

The controller 110 can include a modeling component 115 to generate apassenger connection network 118. The passenger connection network 118,as described below, can map the connections between the scheduledorigination and the scheduled destination for a subset of the set ofpassengers on the flights in the network. The rescheduling component 119can reschedule flights based on a computational analysis of thepassenger connection network 118 by the network decomposition component117 and the network solver component 121 to improve the overall networkflow of passengers through the passenger connection network 118. Thecontroller 110, by way of the rescheduling component 119 can output datarelated to the analysis and updated schedule. The outputs can include,but not be limited to, data related to the set of delayed flights 120along with modified projected departure times, a set of flightcancellations 122 and a set of passengers with missed connections 124.

To generate and exploit a model that considers delay options foroutbound flight legs, a flight disruption component 113 obtains,receives, or otherwise acquires data related to passenger originationand destination (passenger schedule data 116). The flight disruptioncomponent 113 can construct, determine or otherwise generate a passengerconnection network 118 based on the passenger itinerary data, anddetermine, based in part on the passenger connection network 118, anumber of passengers with a given itinerary, a set of flights that aredelayed, a set of modified scheduled departure times, a set of flightcancellations, and a set of possible origination and destinationpassenger flows with misconnections identified. For instance, the flightpassenger connection network 118 can include a flow network which is adirected graph, where the nodes are flight legs and arcs represent validconnections.

For example, in one embodiment, a modeling component 115 can generate apassenger connection network 118 that includes a flight network having aquantity of scheduled flights (V) represented as legs, and a quantity ofscheduled of passenger connections (A) represented as nodes. Forinstance, the flight network can be denoted as G^(p)=(V, A). For a setof inbound flight connections, the modeling component 115 can identifycandidate outbound flights, and add the corresponding flight arc to thepassenger connection network 118. The resulting passenger connectionnetwork 118 can be denoted as {tilde over (G)}^(p)=({tilde over (V)},Ã). The network decomposition component 117 then decomposes the network(e.g., {tilde over (G)}^(p)) into its connected components. Forinstance, the network decomposition component 117 can, for m≥1 connectedcomponents, iterate through connected components {tilde over (G)}₁ ^(p). . . {tilde over (G)}_(m) ^(p). For the connected components {tildeover (G)}_(i) ^(p), a network solver component 121 can expand thenetwork and determines a strategy for moving the passengers through thenetwork of nodes. For example, the network solver component 121 cansolve a modified minimum cost multicommodity network flow problem(MCNFP) over {tilde over (G)}_(i) ^(p) where the commodity in the MCNFPcorresponds to a unique passenger itinerary.

As described above, {tilde over (G)}^(p) is a passenger connectionnetwork whose arcs connect candidate flight legs. FIG. 4 illustrates asimple flight network with five flight legs and four passengerconnections. The initial flight leg 30 connects to three flight legs 32,34, 36. The flight leg 36 connects to flight leg 38, indicating thatsome passengers can connect to the flight leg 38 from the intermediateflight leg 36.

Referring now to FIG. 5, for every passenger connection, the modelingcomponent constructs a passenger connection network 39 with a set ofnodes and arcs. For example, the passenger connection network 39 caninclude six classes of nodes. A source node 40 exists for everyscheduled flight leg that is an initial flight for a connectingpassenger itinerary. A sink node 64 exists for every scheduled flightleg that is a terminating flight for a connecting passenger itinerary.That is, the sink node 64 represents the final destination of apassenger. For every flight leg 42 and 44 having a first projecteddeparture time, flight nodes 46, 48, 50 and 56, 58, 60 are intermediatealternatives of the respective flight leg 42 and 44 associated with adelay option, for example, every flight leg having a second projecteddeparture time later than the first projected departure time. That is,flight leg 42 can depart at its scheduled time per flight node 46, canbe delayed a first duration per flight node 48 or can be delayed alonger second duration per flight node 50. Every flight leg 42 and 44includes a cancellation node 52 and 62 to represent the cancellation ofa given flight, i.e., the second projected departure time can include“null” or a cancellation. Every intermediate alternative represents apossible delay option where the second projected departure time can bepredetermined or selected according to a schedule. A disruption node 54exists for every flight connection to track passengers whose scheduleshave been disrupted due to misconnections or flight cancellations.Additionally, when the passenger connection network 39 includes multiplesink nodes, a super sink node connects to the sink nodes.

In addition, the passenger connection network 39 can include differenttypes of connections between nodes, which are represented as arcs inFIG. 4. An arc 41, 43, 45 connects the source node 40 to a respectiveinitial flight node 46, 48, 50. An arc 47 connects the source node 40 tothe initial flight cancellation node 52. Arcs connect the inbound flightnodes 46, 48, 50 to the outbound flight nodes 56, 58, 60. An arc 49connects inbound flight nodes 46, 48, 50 to outbound flight cancellationnode 62. For every inbound flight node that does not connect to adeparture flight node, an arc 51 connects the inbound flight node to thedisruption node 54. An arc 53 connects the inbound flight cancellationnode 52 to the disruption node 54. An arc 55 connects the disruptionnode 54 to the outbound flight nodes 56, 58, 60. An arc 57 connects thedisruption node 54 to the outbound flight cancellation node 62. An arc59 connects the outbound flight nodes 56, 58, 60 to the sink node 64. Anarc 61 connects the disruption node 62 to the sink node 64.Additionally, when the passenger connection network 39 includes multiplesink nodes, an arc connects the sink nodes to the super sink node.

FIG. 5 shows an example passenger connection network 39 with connectionfrom flight i 42 to flight j 44. That is, for example, passengers arriveto some airport aboard flight i 42 and depart by flight j 44. The flightdisruption component or the modeling component can model the passengerconnection network 39 with the each flight having a first projecteddeparture time, and three departure options for the flights, with thethree departure options having a second projected departure time laterthan the first projected departure time. The first flight time (as inflight nodes 46 and 56) aligns with the projected departure time for therespective flight. Here, it is assumed that flight i 42 constitutes aninitial flight in some itinerary k1 and, consequently, its nodes areincident to some source node s^(k1) 40. Similarly, flight j 44 is aterminal flight for some itinerary k2 and is connected to t^(k2), thesink node 64. Note that itinerary k2 can be itinerary k1.

The disruption node ξ_(ij) 54 is an auxiliary node that tracks anypassengers who experience a disruption (i.e. delay or cancellation) witha scheduled connection from flight i 42 to flight j 44. Because thefirst inbound flight node 46 connects via connection arcs 49A to theoutbound flight nodes 56, 58, 60, no arc connects the first inboundflight node 46 to the disruption node 54. However, if the second inboundflight node 48 (delayed from the first inbound flight node 46) were toconnect to the first outbound flight node 56, then a misconnectionoccurs, hence the processor connects the second inbound flight node 48to the disruption node 54 by arc 51 and ensures balance for the secondinbound flight node 48. Because an inbound flight cancellationautomatically incurs a disruption, the arc 53 connects the cancellationnode 52 to the disruption node 54. Similarly, an arc 55 connects thedisruption node 54 to the outbound flight nodes 56, 58, 60 and an arc 57connects to the cancellation node 62.

Based on the model formulation, the network solver component can applyat least one criterion to the passenger connection network 39 todetermine whether to fly, delay or cancel a flight and how to routepassengers through the passenger connection network 39 given flightdecisions. The network solver component determines whether to fly, delayor cancel a flight by selecting nodes for every flight leg based in parton the set of criteria. The set of criteria form the standard by whichthe network solver determines whether to fly, delay or cancel a flightand form the basis for an objective analysis of the passenger connectionnetwork 39. The set of criteria can be any criterion used for an airlinenetwork flow analysis including but not limited to, aircraft fuel cost,overall airline fuel cost, average per flight departure delay duration,maximum flight departure delay for all flights in the network, perflight revenue, overall flight revenue, the total number of flightcancellations, total flight disruptions for a predetermined set ofpassengers, etc. The network solver component can implement one or moreof the set of criteria with an objective to maximize or minimize one ormore of the set of criteria

The network solver component determines how to route passengers throughthe passenger connection network 39 based on a flow analysis of the arcsin the passenger connection network 39. The objective of the passengerflow problem is to determine appropriate tradeoff costs associated withdelay and cancellation operations and passenger misconnections. At theAOCC, the processed results of the analysis of the passenger connectionnetwork 39 can be integrated with other operations recovery models andwith other objectives such as can be related to tail swaps, ferries,etc.

The network solver component enforces the criteria on the passengerconnection network by associating costs (i.e. weights or penalties) withthe decision variables corresponding to nodes and arcs. With respect tothe nodes, in one implementation, the network solver componentassociates a cost with the flight delay and cancellations nodes (and notthe flight nodes associated with the scheduled times). In oneimplementation, the network solver component can associate a cost to thedelay and cancellation nodes, including, but not limited to, a cost thatis non-decreasing as a function of the severity of the delay. It iscontemplated that other cost functions as applied to the nodes can beimplemented.

Types of costs the network solver component can associate with the arcsinclude, but are not limited to, broken passenger itinerary costs andadditional delay costs. The network solver component assigns a brokenpassenger itinerary cost to any arc incident to the disruption node 54which shows a passenger cannot be assigned an itinerary. The brokenpassenger itinerary cost penalizes solutions that cause passengermisconnections. To conserve flow for the passenger connection network39, any nonzero valued flow input to a node must also flow out.Therefore, to avoid double counting, the network solver componentimposes penalties on the arcs inbound to the disruption node 54 frominbound flight nodes that miss the eligible outbound connections.Penalties are also introduced into arcs from the final cancellation nodeto the sink node 61 as the passenger is assured to be disrupted from acancellation.

The network solver component can assign additional delay costs to arcsbetween flight nodes that result in a longer connection time than thescheduled connection time. That is, the scheduled connection time asrepresented by the arc connecting the scheduled inbound flight node 46to the scheduled outbound flight node 56 is the scheduled duration oftime between the arrival of the inbound flight 42 and the departure ofthe outbound flight 44. The arc 49 connecting the inbound flight nodes46, 48, 50 to the outbound flight nodes 56, 58, 60 includes a durationof time between the arrival of the inbound flight node and the departureof the outbound flight node. For a connection arc that incurs a durationlonger than the scheduled duration, the network solver component canassign a penalty that is a function of the duration of the connectiontime. In one implementation, the network solver component only appliesthe additional delay cost to routes that follow the same airport pairsas on the original itinerary path.

The network solver component can enforce other costs depending upon theimplementation. For example, the processor can assign a cost to arcsinbound to undesired flight nodes to penalize solutions that haveadverse effects on the airline operations. In this example, the cost canbe applied to misconnecting passengers who would be rescheduled on aflight where the aircraft is not explicitly present in an implementedoperations model. In another non-limiting example, the network solvercomponent can enforce a cost on delays that affect local passengers(i.e. passengers with a single itinerary). In this way, the processorbalances the total cost incurred by local and connection passengers.

To solve the passenger connection network 39 for one or more criterionas enforced by the cost functions, the network solver componentsuperimposes passenger itineraries into a single time-space network tosolve a multi-commodity network flow model where original itinerariescorrespond to a commodity. The network solver component can append anoperations model by appending new variables and constraints to passengerconnection network 39.

The network solver component can apply the criterion set forth above tothe passenger connection network and solve a multi-commodity networkflow problem. In one non-limiting formulation of the multi-commoditynetwork flow problem, the network solver component is configured suchthat the objective of the problem is to minimize the costs incurred fromdelays, cancellations and passenger disruptions. In another non-limitingexample, the objective of the problem is to maximize revenue. Thenetwork solver component can solve for one or more objectives with anysuitable optimization framework, including but not limited to mixedinteger linear programming (MILP).

Referring now to FIG. 6, an example flight schedule can provide a simpleexample of a passenger connection network. The flight schedule includesfive flights, a first flight 80 arriving at a first airport 86(indicated by the lower horizontal axis). Passengers on the first flight80 connect at the first airport to one of three outbound flights 82, 84,90 each scheduled with different departure times (as indicated by theintersection of flights 82, 84, 90 with the lower horizontal axis). Thepassengers connecting on the first two outbound flights 82, 84 arrive attheir destinations without additional connections. Passengers on thethird outbound flight 90 are scheduled to connect with a fifth flight 92at a second airport 88 (indicated by the upper horizontal axis).Additionally, passengers originating from the first airport 86 connectfrom the flight 90 to the fifth flight 92 at the second airport 88.

The example includes five unique itineraries for 34 passengers as inTable 1. The first column indicates the number of passengers that sharean itinerary. The second column lists the itinerary according to thescheduled flights for the passengers. Note that the list refers to thefirst flight 80 as “1”, the first outbound flight 82 as “2”, the secondoutbound flight 84 as “3”, the third outbound flight 90 as “4”, and theoutbound flight 92 from the second airport 88 as “5”.

TABLE 1 Number of passengers Itinerary 9 (1, 2) 13 (1, 3) 6 (1, 4) 2 (1,4, 5) 4 (4, 5)

The flight disruption component or the modeling component constructs thepassenger connection network from the flight network shown in FIG. 5 andthe passenger itinerary data shown in TABLE 1 by adding the sourcenodes, the flight nodes, the sink nodes, the connecting arcs etc. asdescribed above. As part of the example, the flight disruption componentor the modeling component includes a disruption event and consequentlyadds delay options for the flights. The delay options for the flightnetwork from FIG. 6 are illustrated in FIG. 7. The flights include ascheduled option (where the flight element is indicated by an appended“A”) and a delay option (where the flight element is indicated by anappended “B”). So, the flight disruption component or the modelingcomponent allows the inbound flight 80 to arrive at its scheduled timeas in 80A or at a re-scheduled delay time 80B. Considering the ninepassengers scheduled on the first outbound flight 82, note that if thenetwork solver component selects a delay for the first inbound flight80B and maintains the original schedule departure for the first outboundflight 82A, the nine passengers will experience a misconnection. Asdescribed above, the flight disruption component or the modelingcomponent tracks misconnections by adding the disruption nodes into thepassenger connection network.

FIG. 8 illustrates an expansion of the passenger connection network forthe example flight network presented in FIGS. 6 and 7. Selecting the setof delays and cancellations for even a simple flight network to mitigatepassenger disruption escalates into a complex process even when theprocessor allows for a single delay for the flights in the network. Theflight disruption component or the modeling component includes thesource nodes b_(s) ¹, b_(s) ², b_(s) ³, b_(s) ⁴, b_(s) ⁵ to representthe passengers at their origination as described by the itinerariesshown in TABLE 1. The flight disruption component or the modelingcomponent models the flights (flight 1-flight 5) to include a node forthe scheduled time, a single delay time, and a cancellation node. Theconnection between flights includes the connecting arcs and anintermediate disruption node to track passengers with disrupteditineraries. The flight disruption component or the modeling componentincludes the sink nodes b_(t) ¹, b_(t) ², b_(t) ³, b_(t) ⁴, b_(t) ⁵ torepresent the passengers at their destination as described by theitineraries shown in TABLE 1. Finally, the flight disruption componentor the modeling component connects the sink nodes to the super sink nodeb_(t) to account for the 34 passengers in the passenger connectionnetwork. As described above, the network solver component solves thenetwork flow problem associated with the passenger connection network toreschedule flights by enforcing delays and cancellations that mitigatepassenger disruption across the network. Referring now to FIG. 9, aflowchart depicting a method 200 of rescheduling flights affected by adisruption according to an embodiment of the invention is illustrated.The method as implemented by a processor includes a first step 210whereby the processor obtains data related to a scheduled originationand a scheduled destination for a set of passengers scheduled on theflights in a flight network. The processor in a second step 212generates a passenger connection network of connections between thescheduled origination and the scheduled destination for a subset of theset of passengers. The connections include at least one connectingflight that is not direct between the scheduled origination and thescheduled destination. Initially, the connecting flight has a firstprojected departure time. The processor at step 214 creates at least oneintermediate alternative of the connecting flights, with theintermediate alternatives having a second projected departure time thatis later than the first projected departure time. The processor addsconnections to the passenger connection network between the scheduledorigination and the scheduled destination for the subset of the set ofpassengers at step 216. The processor applies at least one criterion tothe passenger connection network with the added connections at step 218.At step 220, the processor then solves the network flow problem toreschedule a subset of connecting flights to have a delayed projecteddeparture time that improves the flow of passengers through the flightnetwork. The processor can output data related, but not be limited to,the set of delayed flights 222 along with modified projected departuretimes, a set of flight cancellations 224 and a set of passengers withmissed connections 226.

Technical effects of the above-described embodiments include outputtinga passenger-centric operations recovery solution that minimizespassenger inconveniences due to misconnections, inbound delays andstranding passengers at certain stations. By determining an improvedflight delay or departure time, embodiments of the method and systemprovide re-scheduling options for disrupted passengers usingpre-existing scheduled flights without re-routing passengers. The methodand system presented is amenable to airlines that make operationaldecisions centered on passenger metrics. Passenger modeling within anoperations recovery environment is a computational challenge, and theabove-described embodiments of the system and method have been shownthrough experimental simulation to provide a solution to a complexproblem in an efficient manner and consequently provide a commercialadvantage. To wit, by providing efficient and better overall solutionsfor operations recovery, embodiments of the system and method describedabove ultimately lower the financial cost to an airline during adisruption event. The model and constraints presented above focusprimarily on the passenger connectivity and ignore many otherconstraints that define the operational decisions. The system andmethods described above are readily appended to existing models andstructures to better enhance operational recovery solver systems.

To the extent not already described, the different features andstructures of the various embodiments can be used in combination witheach other as desired. That one feature cannot be illustrated in all ofthe embodiments is not meant to be construed that it cannot be, but isdone for brevity of description. Thus, the various features of thedifferent embodiments can be mixed and matched as desired to form newembodiments, whether or not the new embodiments are expressly described.All combinations or permutations of features described herein arecovered by this disclosure.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and can include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

1.-15. (canceled)
 16. A controller in communication with a databasecontaining data related to a scheduled origination and a scheduleddestination for each of a set of passengers scheduled on aircraftflights, and having software configured to access and retrieve the data;generate a passenger connection network of connections between thescheduled origination and the scheduled destination for a subset of theset of passengers wherein the connections include at least oneconnecting flight that is not direct between the scheduled originationand the scheduled destination, wherein the at least one connectingflight has a first projected departure time; create at least one copy ofthe at least one connecting flight having a second projected departuretime later than the first projected departure time; add connections tothe passenger connection network between the scheduled origination andthe scheduled destination for the subset wherein the added connectionsinclude the at least one copy of the at least one connecting flight;apply at least one criterion to the passenger connection network withthe added connections; and reschedule the at least one connecting flightto at least one second projected departure time based on the at leastone criterion.
 17. The controller of claim 16 wherein the software isfurther configured to determine a set of flights that are delayed, alongwith a modified projected departure time; determine a set of flightcancellations; and identify one or more subsets of passengers withmisconnections.
 18. The controller of claim 16 wherein the software isfurther configured to apply at least one criterion that associates acost to each connection that results in a longer connection time than ascheduled connection time for a subset of the set of passengers.
 19. Thecontroller of claim 16 wherein the software is further configured tosolve a multicommodity network flow problem to apply the at least onecriterion to the passenger connection network.
 20. The controller ofclaim 19 wherein the software is further configured to solve themulticommodity network flow problem with mixed integer linearprogramming.
 21. The controller of claim 16 wherein generating thepassenger connection network includes defining a simulation modelincluding (i) a plurality of flight legs representative of the at leastone connecting flights, (ii) a plurality of nodes for associating eachscheduled origin and schedule destination, and (iii) a plurality ofarcs, each arc connecting two or more of the nodes.
 22. The controllerof claim 21 wherein creating at least one copy of the at least oneconnecting flight includes creating at least one copy of the pluralityof flight legs representative of the at least one connecting flights.23. The controller of claim 21, wherein the controller includes softwarefurther configured to provide a network solver module for analyzing thesimulation model by way of the adding connections and the applying theat least one criterion, and to determine passenger rescheduling optionin responsive to the analysis.
 24. The controller of claim 23 whereinthe determined passenger rescheduling options includes at least one fromthe group including flying, delaying a flight, canceling a flight, androuting passengers in accordance therewith.
 25. The controller of claim23 wherein the controller includes software configured to update aflight schedule for a subset of aircraft flights in accordance with therescheduling, based on passenger connection network analysis.
 26. Thecontroller of claim 25, further comprising flying the subset of aircraftflights in accordance with the updated flight schedule.
 27. Thecontroller of claim 21 wherein the nodes are formed of one or moresource nodes, inbound flight nodes, departure flight nodes, cancellationnodes, disruption nodes, and sink nodes.
 28. The controller of claim 16,wherein the applying at least one criterion includes at least one fromthe group including airline fuel costs, overall airline fuel costs,average per flight departure delay duration, preflight revenue, overallflight revenue, a total number of flight cancellations, and total flightdisruptions for at least a subset of the passengers.
 29. A method ofrescheduling flights, comprising: accessing from a memory storing flightorigination and flight destination data for each of a set of airlinepassengers; generating a flight mapping model by mapping connectionsbetween the flight origination data and the flight destination data in aflight modeling module; generating a passenger flight connectionsimulation model based on the flight mapping model, including (i) aplurality of flight legs representative of scheduled flights, (ii) aplurality of nodes for associating the passenger with the flight legs,and (iii) a plurality of arcs, each arc connecting two or more of thenodes; update a flight schedule for at least a subset of rescheduledflights based on the passenger flight connection simulation model; andflying the at least a subset of reschedule flights in accordance withthe updated flight schedule.
 30. The method of claim 29, furthercomprising analyzing, by a network solver module, the passenger flightconnection simulation model, and determining passenger flightrescheduling options responsive to the analysis.
 31. The method of claim30, wherein the flight rescheduling options include at least one fromthe group including flying, delaying a flight, canceling a flight, androuting passengers in accordance therewith.
 32. The method of claim 30,wherein the determining includes applying at least one criterion to thepassenger flight connection simulation model.
 33. The method of claim32, wherein the criterion includes at least one from the group includingairline fuel costs, overall airline fuel costs, average per flightdeparture delay duration, preflight revenue, overall flight revenue, atotal number of flight cancellations, and total flight disruptions for apre-determined set of passengers.
 34. The method of claim 29, whereingenerating the flight mapping model includes (i) a flight disruptioncomponent that receives the stored flight origination and flightdestination data and (ii) a modeling component that maps the connectionsand produces the passenger flight connection simulation model.
 35. Themethod of claim 34, further comprising decomposing the passengerconnection simulation model into connected components.